A Deep Snore Recognition Approach Integrating Attention Mechanism and Spatio-Temporal Feature Extraction
Year: 2024
Type: conference-paper
Abstract: Snoring, a common sleep issue, can indicate serious sleep-related breathing disorders. To aid in detection and diagnosis, this paper proposes a snore recognition model that integrates attention mechanisms and spatio-temporal feature extraction, known as the Dual Convolutional Layer with Convolutional Block Attention, Channel Splitting, and Gated Recurrent Network (DCSA-GRU), which integrates attention mechanisms and spatio-temporal feature extraction for snore recognition. The model uses dual convolutional layers with different kernel sizes and channel-spatial attention to enhance key information, improving robustness. A channel slicing method captures time-frequency dependencies, while two GRU layers dynamically model feature sequences. This approach yields better classification results compared to existing methods.
Authors: Jihong Wang, Shaoying Dong
Institution: Software Engineering Institute of Guangzhou
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405200359